UNASSIGNED: Whether machine learning (ML) can assist in the diagnosis of Crohn\'s disease (CD) and intestinal tuberculosis (ITB) remains to be explored.
UNASSIGNED: We collected clinical data from 241 patients, and 51 parameters were included. Six ML methods were tested, including logistic regression, decision tree, k-nearest neighbor, multinomial NB, multilayer perceptron, and XGBoost. SHAP and LIME were subsequently introduced as interpretability methods. The ML model was tested in a real-world clinical practice and compared with a multidisciplinary team (MDT) meeting.
UNASSIGNED: XGBoost displays the best performance among the six ML models. The diagnostic AUROC and the accuracy of XGBoost were 0.946 and 0.884, respectively. The top three clinical features affecting our ML model\'s result prediction were T-spot, pulmonary tuberculosis, and onset age. The ML model\'s accuracy, sensitivity, and specificity in clinical practice were 0.860, 0.833, and 0.871, respectively. The agreement rate and kappa coefficient of the ML and MDT methods were 90.7% and 0.780, respectively (P<0.001).
UNASSIGNED: We developed an ML model based on XGBoost. The ML model could provide effective and efficient differential diagnoses of ITB and CD with diagnostic bases. The ML model performs well in real-world clinical practice, and the agreement between the ML model and MDT is strong.

OBJECTIVE: Differentiating intestinal tuberculosis (ITB) from Crohn\'s disease (CD) remains a diagnostic dilemma. Misdiagnosis carries potential grave implications. We aim to establish a multidisciplinary-based model using machine learning approach for distinguishing ITB from CD.
METHODS: Eighty-two patients including 25 patients with ITB and 57 patients with CD were retrospectively recruited (54 in training cohort and 28 in testing cohort). The region of interest (ROI) for the lesion was delineated on magnetic resonance enterography (MRE) and colonoscopy images. Radiomic features were extracted by least absolute shrinkage and selection operator regression. Pathological feature was extracted automatically by deep-learning method. Clinical features were filtered by logistic regression analysis. Diagnostic performance was evaluated by receiver operating characteristic (ROC) curve and decision curve analysis (DCA). Delong\'s test was applied to compare the efficiency between the multidisciplinary-based model and the other four single-disciplinary-based models.
RESULTS: The radiomics model based on MRE features yielded an AUC of 0.87 (95% confidence interval [CI] 0.68-0.96) on the test data set, which was similar to the clinical model (AUC, 0.90 [95% CI 0.71-0.98]) and higher than the colonoscopy radiomics model (AUC, 0.68 [95% CI 0.48-0.84]) and pathology deep-learning model (AUC, 0.70 [95% CI 0.49-0.85]). Multidisciplinary model, integrating 3 clinical, 21 MRE radiomic, 5 colonoscopy radiomic, and 4 pathology deep-learning features, could significantly improve the diagnostic performance (AUC of 0.94, 95% CI 0.78-1.00) on the bases of single-disciplinary-based models. DCA confirmed the clinical utility.
CONCLUSIONS: Multidisciplinary-based model integrating clinical, MRE, colonoscopy, and pathology features was useful in distinguishing ITB from CD.

BACKGROUND: Crohn\'s disease (CD) is often misdiagnosed as intestinal tuberculosis (ITB). However, the treatment and prognosis of these two diseases are dramatically different. Therefore, it is important to develop a method to identify CD and ITB with high accuracy, specificity, and speed.
OBJECTIVE: To develop a method to identify CD and ITB with high accuracy, specificity, and speed.
METHODS: A total of 72 paraffin wax-embedded tissue sections were pathologically and clinically diagnosed as CD or ITB. Paraffin wax-embedded tissue sections were attached to a metal coating and measured using attenuated total reflectance fourier transform infrared spectroscopy at mid-infrared wavelengths combined with XGBoost for differential diagnosis.
RESULTS: The results showed that the paraffin wax-embedded specimens of CD and ITB were significantly different in their spectral signals at 1074 cm-1 and 1234 cm-1 bands, and the differential diagnosis model based on spectral characteristics combined with machine learning showed accuracy, specificity, and sensitivity of 91.84%, 92.59%, and 90.90%, respectively, for the differential diagnosis of CD and ITB.
CONCLUSIONS: Information on the mid-infrared region can reveal the different histological components of CD and ITB at the molecular level, and spectral analysis combined with machine learning to establish a diagnostic model is expected to become a new method for the differential diagnosis of CD and ITB.

This study aimed to develop and evaluate a CT-based deep learning radiomics model for differentiating between Crohn\'s disease (CD) and intestinal tuberculosis (ITB). A total of 330 patients with pathologically confirmed as CD or ITB from the First Affiliated Hospital of Zhengzhou University were divided into the validation dataset one (CD: 167; ITB: 57) and validation dataset two (CD: 78; ITB: 28). Based on the validation dataset one, the synthetic minority oversampling technique (SMOTE) was adopted to create balanced dataset as training data for feature selection and model construction. The handcrafted and deep learning (DL) radiomics features were extracted from the arterial and venous phases images, respectively. The interobserver consistency analysis, Spearman\'s correlation, univariate analysis, and the least absolute shrinkage and selection operator (LASSO) regression were used to select features. Based on extracted multi-phase radiomics features, six logistic regression models were finally constructed. The diagnostic performances of different models were compared using ROC analysis and Delong test. The arterial-venous combined deep learning radiomics model for differentiating between CD and ITB showed a high prediction quality with AUCs of 0.885, 0.877, and 0.800 in SMOTE dataset, validation dataset one, and validation dataset two, respectively. Moreover, the deep learning radiomics model outperformed the handcrafted radiomics model in same phase images. In validation dataset one, the Delong test results indicated that there was a significant difference in the AUC of the arterial models (p = 0.037), while not in venous and arterial-venous combined models (p = 0.398 and p = 0.265) as comparing deep learning radiomics models and handcrafted radiomics models. In our study, the arterial-venous combined model based on deep learning radiomics analysis exhibited good performance in differentiating between CD and ITB.

UNASSIGNED: Standard deep learning methods have been found inadequate in distinguishing between intestinal tuberculosis (ITB) and Crohn\'s disease (CD), a shortcoming largely attributed to the scarcity of available samples. In light of this limitation, our objective is to develop an innovative few-shot learning (FSL) system, specifically tailored for the efficient categorization and differential diagnosis of CD and ITB, using endoscopic image data with minimal sample requirements.
UNASSIGNED: A total of 122 white-light endoscopic images (99 CD images and 23 ITB images) were collected (one ileum image from each patient). A 2-way, 3-shot FSL model that integrated dual transfer learning and metric learning strategies was devised. Xception architecture was selected as the foundation and then underwent a dual transfer process utilizing oesophagitis images sourced from HyperKvasir. Subsequently, the eigenvectors derived from the Xception for each query image were converted into predictive scores, which were calculated using the Euclidean distances to six reference images from the support sets.
UNASSIGNED: The FSL model, which leverages dual transfer learning, exhibited enhanced performance metrics (AUC 0.81) compared to a model relying on single transfer learning (AUC 0.56) across three evaluation rounds. Additionally, its performance surpassed that of a less experienced endoscopist (AUC 0.56) and even a more seasoned specialist (AUC 0.61).
UNASSIGNED: The FSL model we have developed demonstrates efficacy in distinguishing between CD and ITB using a limited dataset of endoscopic imagery. FSL holds value for enhancing the diagnostic capabilities of rare conditions.

Crohn\'s disease (CD) and intestinal tuberculosis (ITB) share similar histopathological characteristics, and differential diagnosis can be a dilemma for pathologists. This study aimed to apply deep learning (DL) to analyze whole slide images (WSI) of surgical resection specimens to distinguish CD from ITB. Overall, 1973 WSI from 85 cases from 3 centers were obtained. The DL model was established in internal training and validated in external test cohort, evaluated by area under receiver operator characteristic curve (AUC). Diagnostic results of pathologists were compared with those of the DL model using DeLong\'s test. DL model had case level AUC of 0.886, 0.893 and slide level AUC of 0.954, 0.827 in training and test cohorts. Attention maps highlighted discriminative areas and top 10 features were extracted from CD and ITB. DL model\'s diagnostic efficiency (AUC = 0.886) was better than junior pathologists (*1 AUC = 0.701, P = 0.088; *2 AUC = 0.861, P = 0.788) and inferior to senior GI pathologists (*3 AUC = 0.910, P = 0.800; *4 AUC = 0.946, P = 0.507) in training cohort. In the test cohort, model (AUC = 0.893) outperformed senior non-GI pathologists (*5 AUC = 0.782, P = 0.327; *6 AUC = 0.821, P = 0.516). We developed a DL model for the classification of CD and ITB, improving pathological diagnosis accuracy effectively.

BACKGROUND: Intestinal tuberculosis is a chronic disease caused by Mycobacterium tuberculosis that mainly affects the ileum and cecum. Small bowel tuberculosis, characterized by predominant involvement of the small intestine, is an extremely rare condition with highly atypical clinical presentations, making diagnosis even more challenging.
METHODS: We report three cases of small intestinal tuberculosis, two of the patients presented primarily with abdominal pain, and one presented with gastrointestinal bleeding. All patients underwent blood tests and imaging examinations. Small bowel endoscopy (SBE) revealed that the main lesions in these patients were intestinal stenosis or gastrointestinal bleeding caused by small intestinal ulcers. One patient ultimately underwent surgical treatment. Following a complex diagnostic process and comprehensive analysis, all patients were confirmed to have small intestinal tuberculosis and received standard antituberculosis treatment, leading to an improvement in their condition.
CONCLUSIONS: Patients with SBTs present with nonspecific symptoms such as abdominal pain, weight loss, and occasional gastrointestinal bleeding. Accurate diagnosis requires a thorough evaluation of clinical symptoms and various tests to avoid misdiagnosis and complications.

UNASSIGNED: The coronavirus disease 2019 (COVID-19) was first reported in Wuhan, Hubei Province, China. It mainly involves the respiratory system, causing fever, cough, chest tightness, and other symptoms. However, when combined with other common or rare diseases, such as appendicitis and intestinal tuberculosis (TB), it can cause other systemic lesions, thus making the original disease lose its specific clinical manifestations. This case highlights the importance of early identification and clinical precision medicine diagnosis and treatment.
UNASSIGNED: A young woman presented with intermittent pain and discomfort in the right lower quadrant. Ultrasonography suggested appendicitis with a peripheral abscess. The nucleic acid test of COVID-19 was positive, and the chest computed tomography scan showed pulmonary involvement. She was sent for surgery. Postoperative body temperature increased regularly, and the TB T-cell test was positive.
UNASSIGNED: Multiple infections caused by common bacteria, pandemic virus, and specific mycobacterium TB cause a series of nonspecific clinical manifestations, which brings challenges to clinical diagnosis and treatment. Therefore, when facing a complex infection case, the authors should consider the possibility of multiple infections and give targeted treatment for the pathogens.
UNASSIGNED: During the epidemic of COVID-19, the incidence of intestinal TB is relatively low, which is easy to be overlooked and misdiagnosed, especially in the case of appendicitis. Therefore, clinicians must be highly vigilant in the diagnosis process to avoid missed diagnosis or misdiagnosis, so as to provide the best diagnosis and treatment plan.

BACKGROUND: Intestinal tuberculosis is a chronic and specific infection caused by Mycobacterium tuberculosis invading the intestine. Due to the nonspecific clinical presentation, it is stressed that intestinal perforation complicates umbilical intestinal fistula and bladder ileal fistula is very rare and extremely difficult to be diagnosed. It is significant to identify the disease and take urgent intervene in the early stage.
METHODS: An 18-month-old boy patient presented with abdominal pain. Abdominal CT suggested abscess formation in the right lower abdomen and pelvis. The patient underwent resection of necrotic and stenotic intestinal segments with the creation of an ileostomy, cystostomy and vesicoureteral fistula repair for the presence of intestinal perforation complicated by vesicoureteral fistula and umbilical enterocutaneous fistula. Histopathology confirmed the intestinal tuberculosis. The patient was discharged successfully after 11 days post anti-tuberculosis treatment.
CONCLUSIONS: Our case report here is a rare case of umbilical intestinal fistula with bladder ileal fistula secondary to intestinal perforation from intestinal tuberculosis. The purpose of this report is to make the surgical community aware of atypical presentations of intestinal tuberculosis. If our peers encounter the similar situation, they can be prepared for corresponding diagnosis and treatment.

Differentiating between Crohn\'s disease (CD) and intestinal tuberculosis (ITB) with endoscopy is challenging. We aim to perform more accurate endoscopic diagnosis between CD and ITB by building a trustworthy AI differential diagnosis application.
A total of 1271 electronic health record (EHR) patients who had undergone colonoscopies at Peking Union Medical College Hospital (PUMCH) and were clinically diagnosed with CD (n = 875) or ITB (n = 396) were used in this study. We build a workflow to make diagnoses with EHRs and mine differential diagnosis features; this involves finetuning the pretrained language models, distilling them into a light and efficient TextCNN model, interpreting the neural network and selecting differential attribution features, and then adopting manual feature checking and carrying out debias training.
The accuracy of debiased TextCNN on differential diagnosis between CD and ITB is 0.83 (CR F1: 0.87, ITB F1: 0.77), which is the best among the baselines. On the noisy validation set, its accuracy was 0.70 (CR F1: 0.87, ITB: 0.69), which was significantly higher than that of models without debias. We also find that the debiased model more easily mines the diagnostically significant features. The debiased TextCNN unearthed 39 diagnostic features in the form of phrases, 17 of which were key diagnostic features recognized by the guidelines.
We build a trustworthy AI differential diagnosis application for differentiating between CD and ITB focusing on accuracy, interpretability and robustness. The classifiers perform well, and the features which had statistical significance were in agreement with clinical guidelines.